With the rapid development of mobile devices, smart assist related applications have become increasingly common. For example, an increasingly popular dialog assistant can dialogue with a user based on voice, text, or other forms to answer questions raised by the user, or simply chat with the user.
In the prior art, questions and answers in a dialog process are regarded as mapping of a question language symbol sequence to an answer language symbol sequence, and a sequence to sequence (seq2seq)-based dialog model is trained using a large amount of manual dialog data with the maximum likelihood estimation as a loss function, so that the dialog model learns a mapping relationship between the question language symbol sequence and the answer language symbol sequence, and dialogs can be automatically generated by the trained dialog model, thereby implementing the dialog assistant.
However, the prior art is susceptible to high frequency reply statements without topic information such as “ok,” “yes,” and “got it” during the model training, resulting in a tendency to produce these low-quality meaningless replies when the trained model is actually used.